The field of medical imaging has seen significant advances since the time X-rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer, more accurate, and more precise machines. However, greater accuracy and precision can result in a dramatic increase in the amount of data produced by a single machine. Due to the large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes used to determine major regions of interest (ROI) in scanned medical images, which can be defined by how an X-ray image is collimated.
Collimation is widely used in X-ray examinations to minimize radiation exposure to patients during an imaging session, and therefore reduces the overall integral dose to the patient. Collimation is also very important to improve overall image quality. Thus, it is necessary to detect collimation and exclude it to optimize the display of the image. Nevertheless, collimation detection remains a very challenging problem due to the large visual variability across collimated images, such as the shape, size, orientation and intensity distribution of the collimated image.
Digital medical images are typically constructed using raw image data obtained from a medical image scanner. Digital medical images are typically either two-dimensional (“2-D”) and made up of pixel elements or three-dimensional (“3-D”) and made up of volume elements (“voxels”). Such 2-D or 3-D images can be processed using medical image recognition techniques to determine features of the image and/or regions of interest. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should determine and crop out non-essential matter in the selected regions of an image for a doctor to better diagnose a disease or condition.
Prior art systems proposed a multi-view learning based method combining region and corner detection. The accuracy of the prior art method highly depended on region segmentation using a two-class pixel-level classification. Because of the large variability of the collimated images and the overlap of feature distribution between the two classes, the accuracy of a two-class classification is limited.
There have been other previous attempts at using boundary detection technique for collimation detection. However, all prior methods used unsupervised models for edge detection, which are based on the assumption that pixels (or voxels) with large gradients and long straight lines are very likely on the boundary of ROI. This assumption cannot hold in many cases, especially when implanted medical devices are captured in the image. Moreover, the prior art methods are constrained to rectangular ROIs, while many images are taken using non-rectangular collimation, such as in the case of circular collimators.